Flexible imputation of missing data

Media type: E-Book

Title: Flexible imputation of missing data

Contributor: Buuren, Stef van [VerfasserIn]

imprint: Boca Raton; London; New York: CRC Press, Taylor & Francis Group, A Chapman & Hall Book, [2018]

Issue: Second edition

Extent: 1 Online-Ressource (433 Seiten)

Language: English

ISBN: 9780429492259; 9780429960352; 9780429960338; 9780429960345

Keywords: Datenanalyse > Fehlende Daten > Imputationstechnik
Multivariate Analyse > Fehlende Daten > R > Zurechnung

Origination:

Footnote:

Description: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface to second edition -- Preface to first edition -- About the author -- List of symbols -- List of algorithms -- I Basics -- 1 Introduction -- 1.1 The problem of missing data -- 1.1.1 Current practice -- 1.1.2 Changing perspective on missing data -- 1.2 Concepts of MCAR, MAR and MNAR -- 1.3 Ad-hoc solutions -- 1.3.1 Listwise deletion -- 1.3.2 Pairwise deletion -- 1.3.3 Mean imputation -- 1.3.4 Regression imputation -- 1.3.5 Stochastic regression imputation -- 1.3.6 LOCF and BOCF -- 1.3.7 Indicator method -- 1.3.8 Summary -- 1.4 Multiple imputation in a nutshell -- 1.4.1 Procedure -- 1.4.2 Reasons to use multiple imputation -- 1.4.3 Example of multiple imputation -- 1.5 Goal of the book -- 1.6 What the book does not cover -- 1.6.1 Prevention -- 1.6.2 Weighting procedures -- 1.6.3 Likelihood-based approaches -- 1.7 Structure of the book -- 1.8 Exercises -- 2 Multiple imputation -- 2.1 Historic overview -- 2.1.1 Imputation -- 2.1.2 Multiple imputation -- 2.1.3 The expanding literature on multiple imputation -- 2.2 Concepts in incomplete data -- 2.2.1 Incomplete-data perspective -- 2.2.2 Causes of missing data -- 2.2.3 Notation -- 2.2.4 MCAR, MAR and MNAR again -- 2.2.5 Ignorable and nonignorable♠ -- 2.2.6 Implications of ignorability -- 2.3 Why and when multiple imputation works -- 2.3.1 Goal of multiple imputation -- 2.3.2 Three sources of variation♠ -- 2.3.3 Proper imputation -- 2.3.4 Scope of the imputation model -- 2.3.5 Variance ratios♠ -- 2.3.6 Degrees of freedom♠ -- 2.3.7 Numerical example -- 2.4 Statistical intervals and tests -- 2.4.1 Scalar or multi-parameter inference? -- 2.4.2 Scalar inference -- 2.4.3 Numerical example -- 2.5 How to evaluate imputation methods -- 2.5.1 Simulation designs and performance measures

2.5.2 Evaluation criteria -- 2.5.3 Example -- 2.6 Imputation is not prediction -- 2.7 When not to use multiple imputation -- 2.8 How many imputations? -- 2.9 Exercises -- 3 Univariate missing data -- 3.1 How to generate multiple imputations -- 3.1.1 Predict method -- 3.1.2 Predict + noise method -- 3.1.3 Predict + noise + parameter uncertainty -- 3.1.4 A second predictor -- 3.1.5 Drawing from the observed data -- 3.1.6 Conclusion -- 3.2 Imputation under the normal linear normal -- 3.2.1 Overview -- 3.2.2 Algorithms♠ -- 3.2.3 Performance -- 3.2.4 Generating MAR missing data -- 3.2.5 MAR missing data generation in multivariate data -- 3.2.6 Conclusion -- 3.3 Imputation under non-normal distributions -- 3.3.1 Overview -- 3.3.2 Imputation from the t-distribution -- 3.4 Predictive mean matching -- 3.4.1 Overview -- 3.4.2 Computational details♠ -- 3.4.3 Number of donors -- 3.4.4 Pitfalls -- 3.4.5 Conclusion -- 3.5 Classification and regression trees -- 3.5.1 Overview -- 3.6 Categorical data -- 3.6.1 Generalized linear model -- 3.6.2 Perfect prediction♠ -- 3.6.3 Evaluation -- 3.7 Other data types -- 3.7.1 Count data -- 3.7.2 Semi-continuous data -- 3.7.3 Censored, truncated and rounded data -- 3.8 Nonignorable missing data -- 3.8.1 Overview -- 3.8.2 Selection model -- 3.8.3 Pattern-mixture model -- 3.8.4 Converting selection and pattern-mixture models -- 3.8.5 Sensitivity analysis -- 3.8.6 Role of sensitivity analysis -- 3.8.7 Recent developments -- 3.9 Exercises -- 4 Multivariate missing data -- 4.1 Missing data pattern -- 4.1.1 Overview -- 4.1.2 Summary statistics -- 4.1.3 Influx and outflux -- 4.2 Issues in multivariate imputation -- 4.3 Monotone data imputation -- 4.3.1 Overview -- 4.3.2 Algorithm -- 4.4 Joint modeling -- 4.4.1 Overview -- 4.4.2 Continuous data -- 4.4.3 Categorical data -- 4.5 Fully conditional specification -- 4.5.1 Overview

4.5.2 The MICE algorithm -- 4.5.3 Compatibility♠ -- 4.5.4 Congeniality or compatibility? -- 4.5.5 Model-based and data-based imputation -- 4.5.6 Number of iterations -- 4.5.7 Example of slow convergence -- 4.5.8 Performance -- 4.6 FCS and JM -- 4.6.1 Relations between FCS and JM -- 4.6.2 Comparisons -- 4.6.3 Illustration -- 4.7 MICE extensions -- 4.7.1 Skipping imputations and overimputation -- 4.7.2 Blocks of variables, hybrid imputation -- 4.7.3 Blocks of units, monotone blocks -- 4.7.4 Tile imputation -- 4.8 Conclusion -- 4.9 Exercises -- 5 Analysis of imputed data -- 5.1 Workflow -- 5.1.1 Recommended workflows -- 5.1.2 Not recommended workflow: Averaging the data -- 5.1.3 Not recommended workflow: Stack imputed data -- 5.1.4 Repeated analyses -- 5.2 Parameter pooling -- 5.2.1 Scalar inference of normal quantities -- 5.2.2 Scalar inference of non-normal quantities -- 5.3 Multi-parameter inference -- 5.3.1 D1 Multivariate Wald test -- 5.3.2 D2 Combining test statistics♠ -- 5.3.3 D3 Likelihood ratio test♠ -- 5.3.4 D1, D2 or D3? -- 5.4 Stepwise model selection -- 5.4.1 Variable selection techniques -- 5.4.2 Computation -- 5.4.3 Model optimism -- 5.5 Parallel computation -- 5.6 Conclusion -- 5.7 Exercises -- II Advanced techniques -- 6 Imputation in practice -- 6.1 Overview of modeling choices -- 6.2 Ignorable or nonignorable? -- 6.3 Model form and predictors -- 6.3.1 Model form -- 6.3.2 Predictors -- 6.4 Derived variables -- 6.4.1 Ratio of two variables -- 6.4.2 Interaction terms -- 6.4.3 Quadratic relations♠ -- 6.4.4 Compositional data♠ -- 6.4.5 Sum scores -- 6.4.6 Conditional imputation -- 6.5 Algorithmic options -- 6.5.1 Visit sequence -- 6.5.2 Convergence -- 6.6 Diagnostics -- 6.6.1 Model fit versus distributional discrepancy -- 6.6.2 Diagnostic graphs -- 6.7 Conclusion -- 6.8 Exercises -- 7 Multilevel multiple imputation -- 7.1 Introduction

7.2 Notation for multilevel models -- 7.3 Missing values in multilevel data -- 7.3.1 Practical issues in multilevel imputation -- 7.3.2 Ad-hoc solutions for multilevel data -- 7.3.3 Likelihood solutions -- 7.4 Multilevel imputation by joint modeling -- 7.5 Multilevel imputation by fully conditional specification -- 7.5.1 Add cluster means of predictors -- 7.5.2 Model cluster heterogeneity -- 7.6 Continuous outcome -- 7.6.1 General principle -- 7.6.2 Methods -- 7.6.3 Example -- 7.7 Discrete outcome -- 7.7.1 Methods -- 7.7.2 Example -- 7.8 Imputation of level-2 variable -- 7.9 Comparative work -- 7.10 Guidelines and advice -- 7.10.1 Intercept-only model, missing outcomes -- 7.10.2 Random intercepts, missing level-1 predictor -- 7.10.3 Random intercepts, contextual model -- 7.10.4 Random intercepts, missing level-2 predictor -- 7.10.5 Random intercepts, interactions -- 7.10.6 Random slopes, missing outcomes and predictors -- 7.10.7 Random slopes, interactions -- 7.10.8 Recipes -- 7.11 Future research -- 8 Individual causal effects -- 8.1 Need for individual causal effects -- 8.2 Problem of causal inference -- 8.3 Framework -- 8.4 Generating imputations by FCS -- 8.4.1 Naive FCS -- 8.4.2 FCS with a prior for ρ -- 8.4.3 Extensions -- 8.5 Bibliographic notes -- III Case studies -- 9 Measurement issues -- 9.1 Too many columns -- 9.1.1 Scientific question -- 9.1.2 Leiden 85+ Cohort -- 9.1.3 Data exploration -- 9.1.4 Outflux -- 9.1.5 Finding problems: loggedEvents -- 9.1.6 Quick predictor selection: quickpred -- 9.1.7 Generating the imputations -- 9.1.8 A further improvement: Survival as predictor variable -- 9.1.9 Some guidance -- 9.2 Sensitivity analysis -- 9.2.1 Causes and consequences of missing data -- 9.2.2 Scenarios -- 9.2.3 Generating imputations under the δ-adjustment -- 9.2.4 Complete-data model -- 9.2.5 Conclusion

9.3 Correct prevalence estimates from self-reported data -- 9.3.1 Description of the problem -- 9.3.2 Don't count on predictions -- 9.3.3 The main idea -- 9.3.4 Data -- 9.3.5 Application -- 9.3.6 Conclusion -- 9.4 Enhancing comparability -- 9.4.1 Description of the problem -- 9.4.2 Full dependence: Simple equating -- 9.4.3 Independence: Imputation without a bridge study -- 9.4.4 Fully dependent or independent? -- 9.4.5 Imputation using a bridge study -- 9.4.6 Interpretation -- 9.4.7 Conclusion -- 9.5 Exercises -- 10 Selection issues -- 10.1 Correcting for selective drop-out -- 10.1.1 POPS study: 19 years follow-up -- 10.1.2 Characterization of the drop-out -- 10.1.3 Imputation model -- 10.1.4 A solution "that does not look good" -- 10.1.5 Results -- 10.1.6 Conclusion -- 10.2 Correcting for nonresponse -- 10.2.1 Fifth Dutch Growth Study -- 10.2.2 Nonresponse -- 10.2.3 Comparison to known population totals -- 10.2.4 Augmenting the sample -- 10.2.5 Imputation model -- 10.2.6 Influence of nonresponse on final height -- 10.2.7 Discussion -- 10.3 Exercises -- 11 Longitudinal data -- 11.1 Long and wide format -- 11.2 SE Fireworks Disaster Study -- 11.2.1 Intention to treat -- 11.2.2 Imputation model -- 11.2.3 Inspecting imputations -- 11.2.4 Complete-data model -- 11.2.5 Results from the complete-data model -- 11.3 Time raster imputation -- 11.3.1 Change score -- 11.3.2 Scientific question: Critical periods -- 11.3.3 Broken stick model♠ -- 11.3.4 Terneuzen Birth Cohort -- 11.3.5 Shrinkage and the change score♠ -- 11.3.6 Imputation -- 11.3.7 Complete-data model -- 11.4 Conclusion -- 11.5 Exercises -- IV Extensions -- 12 Conclusion -- 12.1 Some dangers, some do's and some don'ts -- 12.1.1 Some dangers -- 12.1.2 Some do's -- 12.1.3 Some don'ts -- 12.2 Reporting -- 12.2.1 Reporting guidelines -- 12.2.2 Template -- 12.3 Other applications

12.3.1 Synthetic datasets for data protection

Flexible imputation of missing data - [Second edition]

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